rstWeb - Browser Annotation of Rhetorical Structure Theory

rstWeb is an open source, browser based annotation tool for discourse analyses in Rhetorical Structure Theory. It is meant to support collaborative, online annotation projects using just a web browser, without the need to install software for annotators.

New: Version 2 is now out, supporting screenshots of analyses, annotation scheme warnings with highlighting, and quick XML export, as well as support for Python 3.

Do RST: Online — Collaborative — Browser Based!

For a server installation, rstWeb needs Python 2.6.X or 2.7.X for its backend (Python 3 is now also supported, but less extensively tested), which accesses a SQLite database. The client runs in JavaScript and only needs a browser. User management and configurations use Michael Foord's logintools, and the front end uses the excellent jsPlumb, jQuery and Font Awesome, all of which are packaged with rstWeb and require no installation. To take screenshots of analyses automatically, it is optionally possible to install Selenium and PhantomJS, which automate saving scaled screenshots that fit your analysis size at high quality. This project's source code is available from Github - feel free to fork, contribute, get in touch and let me know what you're working on.

Features

Annotators only need a browser

Single mode structure-editor - no switching between linking, adding spans, multinucs and unlinking

Import .rs3 files from RSTTool or plain text

Export .rs3 format

Support for multiple annotated versions of each document

Download automatically scaled screenshots of your analyses

Admin interface to manage users, assignments, and groups of documents in projects

New:Highlighted warnings for annotation scheme violations

Licensing and availability

rstWeb is open source, freely available and comes with absolutely no warranty under the MIT License.
You can always find the latest release on Github:

Online demo

Example documents

You can get a lot of good examples of RST annotations online from the RST Website. At Georgetown University we are also building
a corpus that includes RST annotation: the GUM corpus. You can download the RST annotations for GUM here: